Thermal infrared imagery is used to find hot spots that indicate human activity. During the daytime, however, these thermal hotspots are easily confused with solar reflections, i.e., glints, from shiny surfaces. Shiny targets look hot in the infrared (IR) spectrum during the daytime because they are directly reflecting sunlight into the camera or sensor. When trying to find hot targets in daytime infrared imagery, however, the glints or specularly reflected sunlight make ambient targets falsely appear hot. It is, therefore, necessary to discriminate between solar glints and thermally active, or hot, areas in order to detect human activity.
In a known approach, glints are differentiated from hot spots by a human, i.e., an image analyst, who uses prior knowledge and context to distinguish ambient targets that are glinting from hot targets. This approach requires special training because the phenomenology of thermal imagery is unfamiliar to the average analyst.
In another approach, glints are differentiated from hot spots by machines using polarization and geometry. The polarization approach, however, requires complicated hardware. The geometric approach assumes a flat world, i.e., it cannot work on tilted targets, and, therefore, is not particularly effective as most of the world is not flat1. 1 The views of the Flat Earth Society notwithstanding.
Another approach is to avoid solar reflections altogether by obtaining IR images at night, leading to very limited opportunities to obtain image information.
A system to distinguish glints from hot spots that is operable during the day is needed.